20.11.2019

Buying traffic with an ROI of 247%: KamaGames promotion experience on the RTB platform

KamaGames company told how BlackJack and Pokerist users were re—attracted, using the example of a joint case with Bidease.

Starting position

Today it is difficult to imagine a company that earns several million dollars a month with iOS and Android and does not resort to retargeting. Founded by immigrants from the CIS, and today officially based in Ireland, KamaGames is no exception.

Instagram Facebook, Instagram, Snapchat and Twitter are the standard retargeting tools used by the publisher to promote their games in the social casino genre.

However, as the company explains, today “it is not enough to solve the problems of returning users to classical sources.” We have to look for new channels.

This explains KamaGames’ desire to work with DSP (Demand Side Platform). Such platforms automatically buy advertising by directly cooperating with its suppliers (direct advertising publishers, advertising networks and SSPs) through the advertising exchange (Ad Exchange). The latter, as a rule, takes place in the format of an auction in real time (RTB — Real-time bidding).

“Programmatik (aka Programmatic Ads Buying — automatic purchase) allows you to use wide settings, starting from attracting a person not only by country, but also by state, city and even any location like a hotel or shopping center, and ending with searching for people in applications by bundle id, limiting the number of impressions per user and even buying according to criteria such as a mobile operator. All this allows you to move to a more granular level of user engagement and, thanks to the most flexible capabilities, get even more high—quality application installations,” KamaGames comments.

As its DSP partner, the publisher is now considering the marketing company Bidease, the experience of working with which will be discussed further.

Goal and campaign setup

The purpose of the advertising campaign, which was launched at the Bidease facilities, was to return paying players who had not been in the game for a long time. There were no additional requirements or tasks (for example, to attract only those who “fell off” more than a year ago, or only “whales”), everyone was returned.

The campaign concerned two applications from the publisher’s portfolio: BlackJack and Pokerist. There were no restrictions on the region, the campaign ran for two weeks around the world. The campaign results were tracked through AppsFlyer tools.

Prior to the launch of the campaign, some preparatory work was carried out. For example, in AdX (Google advertising exchange), products of the Gambling and Simulated Gambling genres are prohibited by default (this is where the social casino falls). But in rare cases, it is possible to get approval for individual advertising formats and countries. Accordingly, an application was submitted and the application passed moderation*. This allowed to increase the volume of impressions of the advertising campaign.

* Developers of games in the genre of social casino KamaGames advises to pass moderation to “clean the page in the store, removing any references to gambling“.

As part of the preparatory work, the frequency of impressions per user was also set (no more than three times a day for each format) and a rule was established — to work with several SSPs at once.

Recall that SSPs are platforms that aggregate offers from sellers of advertising inventory. It is necessary to work with several similar platforms at once in order to exclude intersections within the same campaign.

We selected many sites in one campaign at once in order to specify the frequency of impressions per individual user in the settings. If different sources were used in different campaigns, there would be more likely to be intersections,” KamaGames explains.

Principles of operation

The BlackJack and Pokerist ads had a high bid value each time. Due to this, the win rate (aka Win Rate — the number of impressions won, divided by the number of impressions on which a bet was placed) was about 80%.

KamaGames does not specify how much higher the bid was usually: “We cannot know for sure the bids of others. This data is not broadcast. The difference between low and high eCPM values has been very large for us. We bought only the users we needed, which is why we paid more than others.”

At the same time, the publisher immediately notes that he does not overpay for impressions, despite the work on the auction of the first price (this is when the advertiser pays the price he has set, only first he has to guess the value of the competitor’s bid in order to get ahead of him).

There are no overpayments thanks to the algorithm developed by Bidease. It checks the sensitivity of the received volume at different rates in different sections and allowed KamaGames to buy traffic at the optimal price.

What kind of price is not specified. However, representatives of the company say that, for example, in the USA, the cost of attracting a user to a social casino is on average $ 4-6. In the countries of the second rank, the purchase is already cheaper — $ 3-4.

Working with creatives

The effectiveness of an advertising campaign largely depends on the quality of creatives. When promoting KamaGames resorted to all major formats.

  • Interactive game advertising showed excellent results. Its CTR reached up to 18%.

  • The company was also pleased with the videos. The average CTR of campaigns from video ads was about 13%.

  • Static full-screen banners were also quite a working format, they showed an average CTR of 4%.

The CTR was 3-4 times higher for creatives that depicted new features and in-game events. But there is a nuance. This is only relevant for retargeting campaigns. With an ordinary UA purchase, this approach is less effective.

Final result

The final ROI for the campaign was 247%. KamaGames notes that it would be impossible without:

  • wide coverage;
  • a “sensitive” algorithm that allowed different countries to make the right bet on the same applications and get the maximum audience for the most favorable cost.

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